big o - What is the difference between Θ(n) and O(n)?

Question

Sometimes I see Θ(n) with the strange Θ symbol with something in the middle of it, and sometimes just O(n). Is it just laziness of typing because nobody knows how to type this symbol, or does it mean something different?

Answer 1

Short explanation:

If an algorithm is of Θ(g(n)), it means that the running time of the algorithm as n (input size) gets larger is proportional to g(n).

If an algorithm is of O(g(n)), it means that the running time of the algorithm as n gets larger is at most proportional to g(n).

Normally, even when people talk about O(g(n)) they actually mean Θ(g(n)) but technically, there is a difference.

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More technically:

O(n) represents upper bound. Θ(n) means tight bound. Ω(n) represents lower bound.

f(x) = Θ(g(x)) iff f(x) = O(g(x)) and f(x) = Ω(g(x))

Basically when we say an algorithm is of O(n), it's also O(n²), O(n^1000000), O(2ⁿ), ... but a Θ(n) algorithm is not Θ(n²).

In fact, since f(n) = Θ(g(n)) means for sufficiently large values of n, f(n) can be bound within c₁g(n) and c₂g(n) for some values of c₁ and c₂, i.e. the growth rate of f is asymptotically equal to g: g can be a lower bound and and an upper bound of f. This directly implies f can be a lower bound and an upper bound of g as well. Consequently,

f(x) = Θ(g(x)) iff g(x) = Θ(f(x))

Similarly, to show f(n) = Θ(g(n)), it's enough to show g is an upper bound of f (i.e. f(n) = O(g(n))) and f is a lower bound of g (i.e. f(n) = Ω(g(n)) which is the exact same thing as g(n) = O(f(n))). Concisely,

f(x) = Θ(g(x)) iff f(x) = O(g(x)) and g(x) = O(f(x))

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There are also little-oh and little-omega (ω) notations representing loose upper and loose lower bounds of a function.

To summarize:

f(x) = O(g(x)) (big-oh) means that the growth rate of f(x) is asymptotically less than or equal to to the growth rate of g(x).

f(x) = Ω(g(x)) (big-omega) means that the growth rate of f(x) is asymptotically greater than or equal to the growth rate of g(x)

f(x) = o(g(x)) (little-oh) means that the growth rate of f(x) is asymptotically less than the growth rate of g(x).

f(x) = ω(g(x)) (little-omega) means that the growth rate of f(x) is asymptotically greater than the growth rate of g(x)

f(x) = Θ(g(x)) (theta) means that the growth rate of f(x) is asymptotically equal to the growth rate of g(x)

For a more detailed discussion, you can read the definition on Wikipedia or consult a classic textbook like Introduction to Algorithms by Cormen et al.